Multifractal estimates of monofractality in RR-heart series in power spectrum ranges

Makowiec, Danuta; Dudkowska, A; Gałąska, Rafał; Rynkiewicz, Andrzej

doi:10.1016/j.physa.2009.05.005

Cited by 42 publications

(44 citation statements)

References 43 publications

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“…For these reasons, the more recent research in the field of heart-rate variability is aimed at proposing methods that take into account both the scale dependency of self-similarity and its multifractal nature [12,13]. Following this line of research, the aim of the present study is to describe the multifractal and multiscale characteristics of cardiovascular signals in healthy subjects under controlled conditions.…”

Section: Introductionmentioning

confidence: 99%

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

et al. 2018

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Detrended Fluctuation Analysis (DFA) is a popular method for assessing the fractal characteristics of biosignals, recently adapted for evaluating the heart-rate multifractal and/or multiscale characteristics. However, the existing methods do not consider the beat-by-beat sampling of heart rate and have relatively low scale resolutions and were not applied to cardiovascular signals other than heart rate. Therefore, aim of this work is to present a DFA-based method for joint multifractal/multiscale analysis designed to address the above critical points and to provide the first description of the multifractal/multiscale structure of interbeat intervals (IBI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) in male and female volunteers separately. The method optimizes data splitting in blocks to reduce the DFA estimation variance and to evaluate scale coefficients with Taylor's expansion formulas and maps the scales from beat domains to temporal domains. Applied to cardiovascular signals recorded in 42 female and 42 male volunteers, it showed that scale coefficients and degree of multifractality depend on the temporal scale, with marked differences between IBI, SBP, and DBP and with significant sex differences. Results may be interpreted considering the distinct physiological mechanisms regulating heart-rate and blood-pressure dynamics and the different autonomic profile of males and females.

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Section: Introductionmentioning

confidence: 99%

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

et al. 2018

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“…MF-DFA may be More Consistent than WTMM with Shorter EEG Tracings MF-DFA is an established technique for assessing multifractality, which has been used successfully in several different types of analysis, from simulations [33], to geophysics and ion channels [38], to hydrology [47], to cardiology [43]. It has been described as being comparable in terms of results, but needing less computational power than WTMM [24,33].…”

Section: Discussionmentioning

confidence: 99%

“…In the following, we use the h vs. D(h) naming convention, where h is the Hölder exponent (abscissa) of a fractal subset and D(h) (ordinate) is the corresponding fractal dimension, after [43], rather than the a vs F(a) convention as in [24]. We refer to the graphs of D(h) vs h as MF-DFA and WTMM spectra, depending upon the method used.…”

Section: Multifractal Time Series Analysismentioning

confidence: 99%

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Zorick¹,

Mandelkern²

2013

PsycEXTRA Dataset

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“…Multifractal structures were identified in systems from various areas such as physics [2][3][4][5], biology [6][7][8], chemistry [9,10], economics [11][12][13] and even music [14][15][16][17][18][19]. One of the most popular methods of the multifractal analysis is multifractal detrended correlation (MFDFA) [20][21][22] and cross-correlation analysis (MFCCA) [23,24].…”

Section: Methodsmentioning

confidence: 99%

Quantitative Characteristics of Correlations of Meteorological Data

Rak

Bwanakare

2016

Acta Phys. Pol. A

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This paper presents the quantitative characteristics of correlations (and cross-correlations) of plant main ecofactors i.e. the ground and over-ground temperature, the wind speed, and the humidity. The study is based upon hourly data statistical observations collected in the region of Lublin, in Poland for the period 2001.05. 07-2009.04.10. This paper indicates that plant growth conditions constitute an emergent response to the above direct eco-factors. Then, the dynamics properties of each eco-factor is first analyzed alone for its multifractal structure. We apply the multifractal detrended correlation analysis and multifractal detrended cross-correlation analysis. We show that the widest multifractal spectrum is for over-ground temperature and the strongest power-law cross-correlations exist between ground and over-ground temperature. Next, an impulse response analysis is carried out to measure dynamical inter causalities within all the considered variables. As far as cross-impact between different eco-variables is concerned, one observes that the wind speed, the ground temperature and the air humidity dynamics are the most influenced, in terms of memory length time, by external temperature.

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Multifractal estimates of monofractality in RR-heart series in power spectrum ranges

Cited by 42 publications

References 43 publications

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Quantitative Characteristics of Correlations of Meteorological Data

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